Managing Mass Spectrometry Experiments — experimentFiles • MsExperiment

The MsExperiment class allows the storage and management of all aspects related to a complete proteomics or metabolomics mass spectrometry experiment. This includes experimantal design (i.e. a table with samples), raw mass spectromtry data as spectra and chromatograms, quantitative features, and identification data or any other relevant data files.

For details, see https://rformassspectrometry.github.io/MsExperiment

This package is part of the RforMassSpectrometry initiative: https://www.rformassspectrometry.org/

Usage

experimentFiles(object)

experimentFiles(object) <- value

sampleData(object)

sampleData(object) <- value

qdata(object)

qdata(object) <- value

spectraSampleIndex(x, duplicates = c("first", "keep"))

MsExperiment(
  experimentFiles = MsExperimentFiles(),
  otherData = List(),
  qdata = NULL,
  sampleData = DataFrame(),
  spectra = NULL
)

# S4 method for class 'MsExperiment'
show(object)

# S4 method for class 'MsExperiment'
length(x)

# S4 method for class 'MsExperiment'
spectra(object)

# S4 method for class 'MsExperiment'
spectra(object) <- value

otherData(object)

otherData(object) <- value

linkSampleData(
  object,
  with = character(),
  sampleIndex = seq_len(nrow(sampleData(object))),
  withIndex = integer(),
  subsetBy = 1L
)

# S4 method for class 'MsExperiment,ANY,ANY,ANY'
x[i, j, ..., drop = FALSE]

# S4 method for class 'MsExperiment,function'
filterSpectra(object, filter, ...)

Arguments

object: An instance of class MsExperiment.
value: An object of the appropriate class for the slot to be populated.
x: an MsExperiment.
duplicates: for spectraSampleIndex(): character(1) defining the type of result returned by spectraSampleIndex(). With duplicates = "first" an integer vector is returned with the first match while duplicates = "keep" returns a list of integer with the index of all matches.
experimentFiles: MsExperimentFiles() defining (external) files to data or annotation.
otherData: List with arbitrary additional (other) information or data.
qdata: QFeatures or SummarizedExperiment with the quantification data.
sampleData: DataFrame (or data.frame) with information on individual samples of the experiment.
spectra: Spectra::Spectra() object with the MS spectra data of the experiment.
with: for linkSampleData(): character(1) defining the data to which samples should be linked. See section Linking sample data to other experimental data for details.
sampleIndex: for linkSampleData(): integer with the indices of the samples in sampleData(object) that should be linked.
withIndex: for linkSampleData(): integer with the indices of the elements in with to which the samples (specified by sampleIndex) should be linked to.
subsetBy: for linkSampleData(): optional integer(1) defining the dimension on which the subsetting will occurr on the linked data. Defaults to subsetBy = 1L thus subsetting will happen on the first dimension (rows or elements).
i: for [: an integer, character or logical referring to the indices or names (rowname of sampleData) of the samples to subset.
j: for [: not supported.
...: optional additional parameters. For filterSpectra(): parameters to be passed to the filter function (parameter filter).
drop: for [: ignored.
filter: for filterSpectra(): any filter function supported by Spectra::Spectra() to filter the spectra object (such as filterRt or filterMsLevel). Parameters for the filter function can be passed through ....

Value

See help of the individual functions.

Slots

experimentFiles: An instance of class MsExperimentFiles or NULL.
spectra: An instance of class Spectra or NULL.
qdata: An instance of class QFeatures, SummarizedExperiment or NULL.
otherData: A List to store any additional data objects.
sampleData: A DataFrame documenting the experimental design.
sampleDataLinks: A List with link definitions between samples and data elements. Should not be directly accessed or modified by the user.
metadata: A list to store additional metadata.

General information

An experiment is typically composed of several items

Description and information (covariates etc) of each sample from the experiment. These are stored in the sampleData slot as a DataFrame, each row describing a sample with columns containing all relevant information on that sample.
Files to data or annotations. These are stored in the @experimentFiles slot as an instance of class MsExperimentFiles.
General metadata about the experiment, stored as a list in the @metadata slot.
Mass spectrometry data. Sectra and their metadata are stored as an [Spectra::Spectra()] object in the spectra slot. Chromatographic data is not yet supported but will be stored as a Chromatograms() object in the @chromatorgrams slot.
Quantification data is stored as QFeatures or SummarizedExperiment objects in the @qdata slot and can be accessed or replaced with the qdata() or qdata<- functions, respectively.
Any additional data, be it other spectra data, or proteomics identification data (i.e peptide-spectrum matches defined as PSM objects) can be added as elements to the list stored in the otherData slot.

The length of a MsExperiment is defined by the number of samples (i.e. the number of rows of the object's sampleData). A MsExperiment with two samples will thus have a length of two, independently of the number of files or length of raw data in the object. This also defines the subsetting of the object using the [ function which will always subset by samples. See the section for filtering and subsetting below for more information.

MsExperiment objects can be created using the MsExperiment() function providing the data with the parameters listed below. If the Spectra::Spectra() object provided with the spectra param uses a MsBackendSql backend, sample data could be retrieved from the associated SQL database (see section Using MsExperiment with MsBackendSql in the vignette for details). Alternatively, it is also possible to subsequently add data and information to an existing MsExperiment. Finally, with the readMsExperiment() function it is possible to create a MsExperiment by importing MS spectra data directly from provided data files. See examples below or the package vignette for more information.

Accessing data

Data from an MsExperiment object can be accessed with the dedicated accessor functions:

experimentFiles(), experimentFiles<-: gets or sets experiment files.
length(): get the length of the object which represents the number of samples availble in the object's sampleData.
metadata(), metadata<-: gets or sets the object's metadata.
sampleData(), sampleData<-: gets or sets the object's sample data (i.e. a DataFrame containing sample descriptions).
spectra(), spectra<-: gets or sets spectra data. spectra() returns a Spectra::Spectra() object, spectra<- takes a Spectra data as input and returns the updated MsExperiment.
spectraSampleIndex(): depending on parameter duplicates it returns either an integer (duplicates = "first", the default) or a list (duplicates = "keep") of length equal to the number of spectra within the object with the indices of the sample(s) (in sampleData()) a spectrum is assigned to. With duplicates = "first", an integer with the index is returned for each spectrum. If a spectrum was assigned to more than one sample a warning is shown and only the first sample index is returned for that spectrum. For duplicates = "keep", assignments are returned as a list of integer vectors, each element being the index(es) of the sample(s) a spectrum is assigned to. For spectra that are not linked to any sample an NA_integer_ is returned as index for duplicates = "first" and an empty integer (integer()) for duplicates = "keep". Note that the default duplicates = "first" will work in almost all use cases, as generally, a spectrum will be assigned to a single sample.
qdata(), qdata<-: gets or sets the quantification data, which can be a QFeatures or SummarizedExperiment.
otherData() , otherData<-: gets or sets the addition data types, stored as a List in the object's otherData slot.

Linking sample data to other experimental data

To start with, an MsExperiment is just a loose collection of files and data related to an experiment, no explicit links or associactions are present between the samples and related data. Such links can however be created with the linkSampleData() function. This function can establish links between individual (or all) samples within the object's sampleData to individual, or multiple, data elements or files, such as Spectra or raw data files.

The presence of such links enables a (consistent) subsetting of an MsExperiment by samples. Thus, once the link is defined, any subsetting by sample will also correctly subset the linked data. All other, not linked, data elements are always retained as in the original MsExperiment.

To be able to link different elements within an MsExperiment it is also required to identify them with a consistent naming scheme. The naming scheme of slots and data elements within follows an SQL-like scheme, in which the variable (element) is identified by the name of the database table, followed by a "." and the name of the database table column. For MsExperiment, the naming scheme is defined as "<slot name>.<element name>". A column called "sample_name" within the sampleData data frame can thus be addressed with "sampleData.sample_name", while spectra.msLevel would represent the spectra variable called msLevel within the Spectra stored in the spectra slot.

Links between sample data rows and any other data element are stored as integer matrices within the @sampleDataLinks slot of the object (see also the vignette for examples and illustrations). The first column of a matrix is always the index of the sample, and the second column the index of the element that is linked to that sample, with one row per element. Links can be defined/added with the linkSampleData() function which adds a relationship between rows in sampleData to elements in any other data within the MsExperiment that are specified with parameter with. linkSampleData() supports two different ways to define the link:

Parameter with defines the data to which the link should be established. To link samples to raw data files that would for example be available as a character in an element called "raw_files" within the object's experimentFiles, with = experimentFiles.raw_files would have to be used. Next it is required to specify which samples should be linked with which elements in with. This needs to be defined with the parameters sampleIndex and withIndex, both are expected to be integer vectors specifying which sample in sampleData should be linked to which element in with (see examples below or vignette for examples and details).
As an alternative way, a link could be defined with an SQL-like syntax that relates a column in sampleData to a column/element in the data to which the link should be established. To link for example individual spectra to the corresponding samples with = "sampleData.raw_file = spectra.dataOrigin" could be used assuming that sampleData contains a column named "raw_file" with the (full path) of the raw data file for each sample from which the spectra were imported. In this case both sampleIndex and withIndex can be omitted, but it is expected/required that the columns/elements from sampleData and the data element to which the link should be established contain matching values.

Note that linkSampleData will replace a previously existing link to the same data element.

spectraSampleIndex() is a convenience function that extracts for each spectrum in the object's spectra() the index of the sample it is associated with (see function's help above for more information).

Subsetting and filtering

[: MsExperiment objects can be subset by samples with [i] where i is the index or a logical defining to which samples the data should be subset. Subsetting by sample will (correctly) subset all linked data to the respective samples. If multiple samples are linked to the same data element, subsetting might duplicate that data element. This duplication of n:m relationships between samples to elements does however not affect data consistency (see examples below for more information). Not linked data (slots) will be returned as they are. Subsetting in arbitrary order is supported. See the vignette for details and examples.
filterSpectra(): subsets the Spectra within an MsExperiment using a provided filter function (parameter filter). Parameters for the filter function can be passed with parameter .... Any of the filter functions of a Spectra::Spectra() object can be passed with parameter filter. Possibly present relationships between samples and spectra (links, see also linkSampleData()) are updated. Filtering affects only the spectra data of the object, none of the other slots and data (e.g. sampleData) are modified. The function returns an MsExperiment with the filtered Spectra object.

Author

Laurent Gatto, Johannes Rainer

Examples


## An empty MsExperiment object
msexp <- MsExperiment()
msexp
#> Empty object of class MsExperiment 

example(MsExperimentFiles)
#> 
#> MsExpF> fls <- MsExperimentFiles(mzmls = c("/path/to/f1.mzML", "/path/to/f2.mzML"),
#> MsExpF+                          mzids = "/another/path/to/id1.mzid",
#> MsExpF+                          fasta = "file.fas")
#> 
#> MsExpF> fls
#> MsExperimentFiles of length  3 
#> [["mzmls"]] f1.mzML f2.mzML
#> [["mzids"]] id1.mzid
#> [["fasta"]] file.fas
#> 
#> MsExpF> ## A new MsExperimentFiles containing mzML or mzid files
#> MsExpF> fls[1]
#> MsExperimentFiles of length  1 
#> [["mzmls"]] f1.mzML f2.mzML
#> 
#> MsExpF> fls["mzids"]
#> MsExperimentFiles of length  1 
#> [["mzids"]] id1.mzid
#> 
#> MsExpF> ## The actual file names
#> MsExpF> fls[[1]]
#> [1] "/path/to/f1.mzML" "/path/to/f2.mzML"
#> 
#> MsExpF> fls[[2]]
#> [1] "/another/path/to/id1.mzid"
#> 
#> MsExpF> fls[["fasta"]]
#> [1] "file.fas"
#> 
#> MsExpF> ## None of the files used in this example actually exist
#> MsExpF> existMsExperimentFiles(fls)
#> mzmls: 0 out of 2 exist(s)
#> mzids: 0 out of 1 exist(s)
#> fasta: 0 out of 1 exist(s)
experimentFiles(msexp) <- fls
msexp
#> Object of class MsExperiment 
#>  Files: mzmls, mzids, fasta 

## Linking samples to data elements

## Create a small experiment
library(S4Vectors)
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: generics
#> 
#> Attaching package: ‘generics’
#> The following objects are masked from ‘package:base’:
#> 
#>     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
#>     setequal, union
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
#>     mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#>     rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
#>     unsplit, which.max, which.min
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
mse <- MsExperiment()
sd <- DataFrame(sample_id = c("QC1", "QC2"),
                sample_name = c("QC Pool", "QC Pool"),
                injection_idx = c(1, 3))
sampleData(mse) <- sd

## define file names containing spectra data for the samples and
## add them, along with other arbitrary files to the experiment
fls <- dir(system.file("sciex", package = "msdata"), full.names = TRUE)
experimentFiles(mse) <- MsExperimentFiles(
    mzML_files = fls,
    annotations = "internal_standards.txt")

## Link samples to data files: first sample to first file in "mzML_files",
## second sample to second file in "mzML_files"
mse <- linkSampleData(mse, with = "experimentFiles.mzML_files",
    sampleIndex = c(1, 2), withIndex = c(1, 2))

## Link all samples to the one file in "annotations"
mse <- linkSampleData(mse, with = "experimentFiles.annotations",
    sampleIndex = c(1, 2), withIndex = c(1, 1))
mse
#> Object of class MsExperiment 
#>  Files: mzML_files, annotations 
#>  Experiment data: 2 sample(s)
#>  Sample data links:
#>   - experimentFiles.mzML_files: 2 sample(s) to 2 element(s).
#>   - experimentFiles.annotations: 2 sample(s) to 1 element(s).

## Import the spectra data and add it to the experiment
library(Spectra)
#> Loading required package: BiocParallel
spectra(mse) <- Spectra(fls, backend = MsBackendMzR())

## Link each spectrum to the respective sample. We use the alternative
## link definition that does not require sampleIndex and withIndex but
## links elements based on matching values in the specified data elements.
## We need to add the full file name as an additional column to sampleData
## in order to allow matching this file names with the value in
## spectra(mse)$dataOrigin which contains the original file names from which
## the spectra were imported.
sampleData(mse)$raw_file <- normalizePath(fls)

## The links can be added using the short notation below
mse <- linkSampleData(mse, with = "sampleData.raw_file = spectra.dataOrigin")
mse
#> Object of class MsExperiment 
#>  Files: mzML_files, annotations 
#>  Spectra: MS1 (1862) 
#>  Experiment data: 2 sample(s)
#>  Sample data links:
#>   - experimentFiles.mzML_files: 2 sample(s) to 2 element(s).
#>   - experimentFiles.annotations: 2 sample(s) to 1 element(s).
#>   - spectra: 2 sample(s) to 1862 element(s).

## With sampleData links present, any subsetting of the experiment by sample
## will ensure that all linked elements are subset accordingly
b <- mse[2]
b
#> Object of class MsExperiment 
#>  Files: mzML_files, annotations 
#>  Spectra: MS1 (931) 
#>  Experiment data: 1 sample(s)
#>  Sample data links:
#>   - experimentFiles.mzML_files: 1 sample(s) to 1 element(s).
#>   - experimentFiles.annotations: 1 sample(s) to 1 element(s).
#>   - spectra: 1 sample(s) to 931 element(s).
sampleData(b)
#> DataFrame with 1 row and 4 columns
#>     sample_id sample_name injection_idx      raw_file
#>   <character> <character>     <numeric>   <character>
#> 1         QC2     QC Pool             3 /__w/_temp...
experimentFiles(b)$mzML_files
#> [1] "/__w/_temp/Library/msdata/sciex/20171016_POOL_POS_3_105-134.mzML"

## The `spectraSampleIndex()` function returns, for each spectrum, the
## index in the object's `sampleData` to which it is linked/assigned
spectraSampleIndex(mse)
#>    [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>   [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>   [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [926] 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#>  [963] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1000] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1037] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1074] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1111] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1148] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1185] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1222] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1259] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1296] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1333] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1370] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1407] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1444] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1481] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1518] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1555] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1592] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1629] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1666] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1703] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1740] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1777] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1814] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [1851] 2 2 2 2 2 2 2 2 2 2 2 2

## Subsetting with duplication of n:m sample to data relationships
##
## Both samples were assigned above to one "annotation" file in
## `experimentFiles`:
experimentFiles(mse[1])[["annotations"]]
#> [1] "internal_standards.txt"
experimentFiles(mse[2])[["annotations"]]
#> [1] "internal_standards.txt"

## Subsetting will always keep the relationship between samples and linked
## data elements. Subsetting will however possibly duplicate data elements
## that are shared among samples. Thus, while in the original object the
## element "annotations" has a single entry, subsetting with [1:2] will
## result in an MsExperiment with duplicated entries in "annotations"
experimentFiles(mse)[["annotations"]]
#> [1] "internal_standards.txt"
experimentFiles(mse[1:2])[["annotations"]]
#> [1] "internal_standards.txt" "internal_standards.txt"

## Spectra within an MsExperiment can be filtered/subset with the
## `filterSpectra` function and any of the filter functions supported
## by `Spectra` objects. Below we restrict the spectra data to spectra
## with a retention time between 200 and 210 seconds.
res <- filterSpectra(mse, filterRt, rt = c(200, 210))
res
#> Object of class MsExperiment 
#>  Files: mzML_files, annotations 
#>  Spectra: MS1 (72) 
#>  Experiment data: 2 sample(s)
#>  Sample data links:
#>   - experimentFiles.mzML_files: 2 sample(s) to 2 element(s).
#>   - experimentFiles.annotations: 2 sample(s) to 1 element(s).
#>   - spectra: 2 sample(s) to 72 element(s).

## The object contains now much less spectra. The retention times for these
rtime(spectra(res))
#>  [1] 200.049 200.328 200.607 200.886 201.165 201.444 201.723 202.002 202.281
#> [10] 202.560 202.839 203.118 203.397 203.676 203.955 204.234 204.513 204.792
#> [19] 205.071 205.350 205.629 205.908 206.187 206.466 206.745 207.024 207.303
#> [28] 207.582 207.861 208.140 208.419 208.698 208.977 209.256 209.535 209.814
#> [37] 200.044 200.323 200.602 200.881 201.160 201.439 201.718 201.997 202.276
#> [46] 202.555 202.834 203.113 203.392 203.671 203.950 204.229 204.508 204.787
#> [55] 205.066 205.345 205.624 205.903 206.182 206.461 206.740 207.019 207.298
#> [64] 207.577 207.856 208.135 208.414 208.693 208.972 209.251 209.530 209.809

## Relationship between samples and spectra was preserved by the filtering
a <- res[1L]
spectra(a)
#> MSn data (Spectra) with 36 spectra in a MsBackendMzR backend:
#>       msLevel     rtime scanIndex
#>     <integer> <numeric> <integer>
#> 1           1   200.049       717
#> 2           1   200.328       718
#> 3           1   200.607       719
#> 4           1   200.886       720
#> 5           1   201.165       721
#> ...       ...       ...       ...
#> 32          1   208.698       748
#> 33          1   208.977       749
#> 34          1   209.256       750
#> 35          1   209.535       751
#> 36          1   209.814       752
#>  ... 34 more variables/columns.
#> 
#> file(s):
#> 20171016_POOL_POS_1_105-134.mzML
#> Processing:
#>  Filter: select retention time [200..210] on MS level(s)  [Wed Jun  4 06:39:29 2025]